code: def classification_binary(self):
      from sklearn.metrics import (
          accuracy_score,
          f1_score,
          precision_score,
          recall_score,
          roc_auc_score,
      )
  
      true, pred_score = torch.cat(self._true), torch.cat(self._pred)
      pred_int = self._get_pred_int(pred_score)
      try:
          r_a_score = roc_auc_score(true, pred_score)
      except ValueError:
          r_a_score = 0.0
      return {
          "accuracy": round(accuracy_score(true, pred_int), cfg.round),
          "precision": round(
              precision_score(true, pred_int), cfg.round
          ),
          "recall": round(recall_score(true, pred_int), cfg.round),
          "f1": round(f1_score(true, pred_int), cfg.round),
          "auc": round(r_a_score, cfg.round),
      }
  
imports: import logging
  import math
  import sys
  import torch
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.utils.device import get_current_gpu_usage
  from graphgym.utils.io import dict_to_json, dict_to_tb, makedirs
label_desc: Use image node_id 8 for calling the Logger obj's underlying member method code with example usage: obj.classification_binary() and returns: dict